{
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.13991367 0.01410133 0.33966798 0.50631702]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        50\n",
      "  versicolor       0.92      0.94      0.93        50\n",
      "   virginica       0.94      0.92      0.93        50\n",
      "\n",
      "   micro avg       0.95      0.95      0.95       150\n",
      "   macro avg       0.95      0.95      0.95       150\n",
      "weighted avg       0.95      0.95      0.95       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "data = load_iris()\n",
    "\n",
    "X, y = data[\"data\"], data[\"target\"]\n",
    "\n",
    "clf = joblib.load('classification.pkl')\n",
    "\n",
    "print(clf.feature_importances_)\n",
    "print(classification_report(y, clf.predict(\n",
    "    X), target_names=data[\"target_names\"]))\n"
   ]
  }
 ],
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